Modeling the role of fixational eye movements in real-world scenes

Biologically-inspired image processing in computational retina models

Retinal Prosthesis (RP) is an approach to restore vision, using an implanted device to electrically stimulate the retina. A fundamental problem in RP is to translate the visual scene to retina neural spike patterns, mimicking the computations normally done by retina neural circuits. Towards the perspective of improved RP interventions, we propose a Computer Vision (CV) image preprocessing method based on Retinal Ganglion Cells functions and then use the method to reproduce retina output with a standard Generalized Integrate & Fire (GIF) neuron model. "Virtual Retina" simulation software is used to provide the stimulus-retina response data to train and test our model. We use a sequence of natural images as model input and show that models using the proposed CV image preprocessing outperform models using raw image intensity (interspike-interval distance 0.17 vs 0.27). This result is aligned with our hypothesis that raw image intensity is an improper image representation for Retinal Ganglion Cells response prediction.

Development of visual Neuroprostheses: trends and challenges

Visual prostheses are implantable medical devices that are able to provide some degree of vision to individuals who are blind. This research field is a challenging subject in both ophthalmology and basic science that has progressed to a point where there are already several commercially available devices. However, at present, these devices are only able to restore a very limited vision, with relatively low spatial resolution. Furthermore, there are still many other open scientific and technical challenges that need to be solved to achieve the therapeutic benefits envisioned by these new technologies. This paper provides a brief overview of significant developments in this field and introduces some of the technical and biological challenges that still need to be overcome to optimize their therapeutic success, including long-term viability and biocompatibility of stimulating electrodes, the selection of appropriate patients for each artificial vision approach, a better understanding of brain plasticity and the development of rehabilitative strategies specifically tailored for each patient.